{"id":7271,"date":"2023-11-10T06:57:38","date_gmt":"2023-11-10T06:57:38","guid":{"rendered":"https:\/\/dailyai.com\/?p=7271"},"modified":"2023-11-10T07:08:17","modified_gmt":"2023-11-10T07:08:17","slug":"nvidia-sets-new-ai-training-records-in-mlperf-benchmarks","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/","title":{"rendered":"Nvidia \u00e9tablit de nouveaux records d'apprentissage de l'IA dans les benchmarks MLPerf"},"content":{"rendered":"

Nvidia a mis \u00e0 l'\u00e9chelle et optimis\u00e9 son supercalculateur d'IA Eos pour \u00e9tablir de nouveaux records de r\u00e9f\u00e9rence en mati\u00e8re d'apprentissage de l'IA MLPerf.<\/strong><\/p>\n

Lorsque Nvidia a allum\u00e9 son supercalculateur d'IA Eos en mai, 10 752 GPU NVIDIA H100 Tensor Core ont pris vie et viennent d'\u00eatre soumis \u00e0 des tests en conditions r\u00e9elles.<\/p>\n

Cette puissance de traitement sans pr\u00e9c\u00e9dent, associ\u00e9e \u00e0 de nouvelles optimisations logicielles, a permis \u00e0 Eos de repousser les limites de la technologie MLPerf rep\u00e8re<\/a> en territoire record.<\/p>\n

Le benchmark MLPerf open-source est un ensemble de tests de formation et d'inf\u00e9rence con\u00e7us pour mesurer les performances des charges de travail d'apprentissage automatique sur des ensembles de donn\u00e9es du monde r\u00e9el.<\/p>\n

L'un des r\u00e9sultats les plus remarquables est qu'Eos a \u00e9t\u00e9 en mesure d'entra\u00eener un mod\u00e8le GPT-3 avec 175 milliards de param\u00e8tres sur un milliard de jetons en seulement 3,9 minutes.<\/p>\n

Lorsque Nvidia a \u00e9tabli le record sur ce benchmark il y a moins de 6 mois, il a fallu presque 3 fois plus de temps avec un temps de 10,9 minutes.<\/p>\n

Nvidia a \u00e9galement r\u00e9ussi \u00e0 atteindre un taux d'efficacit\u00e9 de 93% pendant les tests, ce qui signifie qu'elle a utilis\u00e9 presque toute la puissance de calcul th\u00e9oriquement disponible dans Eos.<\/p>\n

Microsoft Azure, qui utilise pratiquement la m\u00eame configuration H100 qu'Eos dans sa machine virtuelle ND H100 v5, est arriv\u00e9 \u00e0 2% des r\u00e9sultats de Nvidia dans ses tests MLPerf.<\/p>\n

\"\"
Mat\u00e9riel Nvidia utilis\u00e9 dans les tests MLPerf. Source : Nvidia : Nvidia<\/figcaption><\/figure>\n

En 2018, Jensen Huang, PDG de Nvidia, a d\u00e9clar\u00e9 que les performances des GPU allaient plus que doubler tous les deux ans. Cette affirmation a \u00e9t\u00e9 baptis\u00e9e \"loi de Haung\" et s'est av\u00e9r\u00e9e exacte puisqu'elle laisse Disparition de la loi de Moore<\/a> dans le r\u00e9troviseur informatique.<\/p>\n

Et alors ?<\/h2>\n

Le test d'entra\u00eenement MLPerf que Nvidia a r\u00e9ussi n'utilise qu'une partie de l'ensemble des donn\u00e9es sur lesquelles GPT-3 a \u00e9t\u00e9 entra\u00een\u00e9. Si l'on prend la dur\u00e9e fix\u00e9e par Eos pour le test MLPerf et que l'on extrapole pour l'ensemble du jeu de donn\u00e9es GPT-3, il est possible d'entra\u00eener le mod\u00e8le complet en seulement 8 jours.<\/p>\n

Si vous essayez de le faire en utilisant son ancien syst\u00e8me de pointe compos\u00e9 de 512 GPU A100, cela prendrait environ 170 jours.<\/p>\n

Si vous deviez former un nouveau mod\u00e8le d'IA, pouvez-vous imaginer la diff\u00e9rence de d\u00e9lai de mise sur le march\u00e9 et de co\u00fbt que repr\u00e9sente un d\u00e9lai de 8 jours par rapport \u00e0 un d\u00e9lai de 170 jours ?<\/p>\n

Les GPU H100 ne sont pas seulement beaucoup plus puissants que les GPU A100, ils sont jusqu'\u00e0 3,5 fois plus \u00e9conomes en \u00e9nergie. Consommation d'\u00e9nergie et L'empreinte carbone de l'IA<\/a> sont des questions r\u00e9elles qui doivent \u00eatre abord\u00e9es.<\/p>\n

Pour avoir une id\u00e9e de la rapidit\u00e9 avec laquelle le traitement de l'IA s'am\u00e9liore, il suffit de penser \u00e0 ChatGPT, qui a \u00e9t\u00e9 mis en service il y a un peu moins d'un an. Le mod\u00e8le sous-jacent, GPT-3, a \u00e9t\u00e9 entra\u00een\u00e9 sur 10 240 GPU Nvidia V100.<\/p>\n

Moins d'un an plus tard, l'Eos dispose d'une puissance de traitement 28 fois sup\u00e9rieure \u00e0 celle de cette configuration et d'une efficacit\u00e9 3,5 fois sup\u00e9rieure.<\/p>\n

\"\"
Puissance de traitement qui a form\u00e9 GPT-3 vs Nvidia Eos<\/figcaption><\/figure>\n

Lorsque Sam Altman, d'OpenAI, a conclu le r\u00e9cent DevDay, il a d\u00e9clar\u00e9 que les projets sur lesquels OpenAI travaillait feraient para\u00eetre ses derni\u00e8res versions d\u00e9su\u00e8tes.<\/p>\n

Compte tenu des progr\u00e8s r\u00e9alis\u00e9s par des entreprises comme Nvidia en mati\u00e8re de puissance de traitement, l'affirmation de M. Altman r\u00e9sume probablement l'avenir de l'industrie de l'IA dans son ensemble.<\/p>","protected":false},"excerpt":{"rendered":"

Nvidia a mis \u00e0 l'\u00e9chelle et optimis\u00e9 son supercalculateur d'IA Eos pour \u00e9tablir de nouveaux records de r\u00e9f\u00e9rence pour l'apprentissage de l'IA MLPerf. Lorsque Nvidia a allum\u00e9 son supercalculateur d'IA Eos en mai, 10 752 GPU NVIDIA H100 Tensor Core ont pris vie et viennent d'\u00eatre soumis \u00e0 des tests en conditions r\u00e9elles. Cette puissance de traitement sans pr\u00e9c\u00e9dent, associ\u00e9e \u00e0 de nouvelles optimisations logicielles, a permis \u00e0 Eos de faire passer le benchmark MLPerf en territoire record. Le benchmark MLPerf open-source est un ensemble de tests de formation et d'inf\u00e9rence con\u00e7us pour mesurer les performances des charges de travail d'apprentissage automatique sur des ensembles de donn\u00e9es r\u00e9els. L'un des r\u00e9sultats les plus remarquables est qu'Eos a pu<\/p>","protected":false},"author":6,"featured_media":973,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[106],"class_list":["post-7271","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-nvidia"],"yoast_head":"\nNvidia sets new AI training records in MLPerf benchmarks | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/fr\/2023\/11\/nvidia-sets-new-ai-training-records-in-mlperf-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Nvidia sets new AI training records in MLPerf benchmarks | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Nvidia scaled and optimized its Eos AI supercomputer to set new MLPerf AI training benchmark records. When Nvidia switched its AI supercomputer Eos on in May, 10,752 NVIDIA H100 Tensor Core GPUs came to life and they’ve just been put through some real-world test trials. This unprecedented processing power along with new software optimizations has enabled Eos to push the MLPerf benchmark into record territory. The open-source MLPerf benchmark is a set of training and inference tests designed to measure the performance of machine learning workloads on real-world datasets. 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